Epidemiology — MCQs

Epidemiology Indian Medical PG Practice Questions and MCQs

Question 1671: In a study on the impact of smoking on lung cancer, a relative risk of 8 is found. What does this imply?

A. Smoking increases the risk of lung cancer by 8 times (Correct Answer)
B. There is no association between smoking and lung cancer
C. Smoking decreases the risk of lung cancer
D. 8% of lung cancer cases are due to smoking

Explanation: ***Smoking increases the risk of lung cancer by 8 times*** - A **relative risk (RR)** of 8 indicates that the incidence rate of lung cancer in smokers is 8 times higher than in non-smokers. - This value represents a **strong positive association** between smoking and the development of lung cancer. *There is no association between smoking and lung cancer* - A relative risk value of 1 would indicate **no association** between the exposure (smoking) and the outcome (lung cancer). - A relative risk of 8 signifies a **substantial positive association**, directly contrasting with no association. *Smoking decreases the risk of lung cancer* - A relative risk **less than 1** (e.g., 0.5) would suggest that smoking *decreases* the risk of lung cancer. - Since the reported relative risk is 8, it clearly indicates an **increased risk**, not a decreased risk. *8% of lung cancer cases are due to smoking* - This statement refers to **attributable risk percentage**, which is a different epidemiological measure. - Attributable risk percentage quantifies the proportion of disease cases in an exposed group that can be attributed to the exposure, not the magnitude of increased risk.

Question 1672: In the context of diagnostic testing, how does using a series testing approach affect the specificity and sensitivity of the test?

A. Increases specificity and decreases sensitivity (Correct Answer)
B. Increases sensitivity and decreases specificity
C. Both increase
D. Both decrease

Explanation: ***Increases specificity and decreases sensitivity*** - A **series testing approach** means that for a diagnosis to be made, **all tests in the sequence must be positive**. This approach is designed to reduce false positives because a patient must pass multiple hurdles. - By requiring multiple positive results, the likelihood of a false positive for the overall diagnosis decreases, thereby **increasing the specificity** of the diagnostic process. Conversely, this stricter criterion means some true positives might be missed (if one test in the series is negative, even if the patient has the disease), leading to a **decrease in sensitivity**. *Increases sensitivity and decreases specificity* - This outcome is characteristic of a **parallel testing approach**, where a positive result on **any one of several tests** is sufficient for a diagnosis. - While parallel testing increases the chance of catching true positives (higher sensitivity), it also raises the risk of false positives (lower specificity) because fewer criteria need to be met. *Both increase* - It is generally **not possible** to increase both sensitivity and specificity simultaneously through a simple change in testing strategy without altering the intrinsic properties of the tests themselves. - There is typically an **inverse relationship** between sensitivity and specificity; improving one often comes at the expense of the other. *Both decrease* - A decrease in both sensitivity and specificity would indicate a **poorly designed or executed testing strategy**, or using tests that are individually unreliable. - This outcome would be undesirable as it would lead to both a high rate of missed diagnoses and a high rate of false positive diagnoses.

Question 1673: A study is conducted to assess the relationship between smoking and lung cancer by following a group of smokers and non-smokers over time to calculate incidence rates. What type of study design is being described?

A. Case-control study
B. Cohort study (Correct Answer)
C. Cross-sectional study
D. Randomized controlled trial

Explanation: ***Cohort study*** - A **cohort study** follows a group of individuals over time based on their exposure status (smokers vs. non-smokers) to see who develops the outcome (lung cancer). - This design allows for the calculation of **incidence rates** and **relative risk**, providing strong evidence for causality and temporal relationships. - Cohort studies are considered the **gold standard** for observational studies as they establish that exposure precedes disease. *Case-control study* - A **case-control study** starts with individuals who already have the outcome (lung cancer cases) and compares their past exposure (smoking) to a control group without the outcome. - While efficient for rare diseases and diseases with long latency periods, the question specifically describes **following subjects over time**, which is characteristic of a cohort design, not case-control. *Cross-sectional study* - A **cross-sectional study** assesses exposure and outcome simultaneously at a single point in time, providing a snapshot of prevalence. - It cannot establish a **temporal relationship** between smoking and lung cancer, and does not involve following subjects over time. *Randomized controlled trial* - A **randomized controlled trial (RCT)** involves randomly assigning participants to an intervention or control group and is best for evaluating the effectiveness of treatments or preventive interventions. - It would be **unethical** to randomize individuals to smoke to assess lung cancer risk, making this design inappropriate for studying harmful exposures.

Question 1674: A study reports a confidence interval (CI) for a relative risk (RR) ranging from 0.8 to 1.2. How should this result be interpreted in relation to the association between exposure and outcome?

A. No significant association exists between the exposure and outcome. (Correct Answer)
B. The exposure reduces the risk of the outcome.
C. The study may have low statistical power.
D. The exposure increases the risk of the outcome.

Explanation: ***No significant association exists between the exposure and outcome.*** - A confidence interval for **relative risk (RR)** that includes **1.0** indicates that there is **no statistically significant association** between the exposure and outcome. - The interval (0.8 to 1.2) encompasses both values less than 1 (suggesting reduced risk) and values greater than 1 (suggesting increased risk), making it impossible to definitively conclude an effect. *The exposure reduces the risk of the outcome.* - This would only be true if the **entire confidence interval** for the relative risk was **below 1.0** (e.g., 0.6 to 0.9). - Since the interval extends above 1.0, a protective effect cannot be concluded. *The study may have low statistical power.* - While a **wide confidence interval** can sometimes suggest **low statistical power**, the primary interpretation of an interval that includes 1.0 is the **absence of a significant effect**. - Without additional information, it's not the first or most direct conclusion from the given CI. *The exposure increases the risk of the outcome.* - This would only be true if the **entire confidence interval** for the relative risk was **above 1.0** (e.g., 1.1 to 1.5). - As the interval includes values below 1.0, an increased risk cannot be definitively concluded.

Question 1675: In a cohort study, which measure is most appropriate for comparing the risk of developing a disease between exposed and non-exposed groups?

A. Odds ratio
B. Relative risk (Correct Answer)
C. Absolute risk
D. Incidence rate

Explanation: ***Relative risk*** - The **relative risk (RR)** directly compares the **incidence of disease** in the exposed group to the incidence in the non-exposed group in a **cohort study**. - It quantifies how many times more likely (or less likely) the exposed group is to develop the disease compared to the unexposed group. *Odds ratio* - The **odds ratio (OR)** is primarily used in **case-control studies** to estimate the association between exposure and outcome. - While it can approximate the relative risk when the disease is rare, it is not the most direct measure of risk comparison in a cohort study. *Absolute risk* - **Absolute risk** refers to the **incidence of the disease** in a specific group and does not inherently involve a comparison between exposed and non-exposed groups. - It represents the probability of developing the disease over a specified period. *Incidence rate* - **Incidence rate** measures the **frequency of new cases** of a disease in a population over a given period. - While essential for calculating relative risk, incidence rate itself is a measure of occurrence, not a direct comparative measure between two groups.

Question 1676: The specificity of a screening test for a disease is 95%. What does this imply?

A. The test correctly identifies 95% of the true negatives. (Correct Answer)
B. The test correctly identifies 95% of the true positives.
C. The test misses 95% of the true cases.
D. 95% of the people who test negative are disease-free.

Explanation: ***The test correctly identifies 95% of the true negatives.*** - **Specificity** is defined as the proportion of **true negatives** (individuals without the disease who test negative) correctly identified by the test. - A 95% specificity means that 95% of healthy individuals (without the disease) will correctly test negative. *The test correctly identifies 95% of the true positives.* - This statement describes **sensitivity**, not specificity. **Sensitivity** refers to the test's ability to correctly identify individuals who *do* have the disease (true positives). - High sensitivity is crucial for ruling out a disease when a test result is negative. *The test misses 95% of the true cases.* - This suggests a very low sensitivity (5%), which is not what specificity measures. - Missing 95% of true cases would indicate a high rate of **false negatives**. *95% of the people who test negative are disease-free.* - This statement refers to the **Negative Predictive Value (NPV)**, which is the probability that a person who tests negative truly does not have the disease. - NPV is influenced by both specificity and disease prevalence, and it's not synonymous with specificity itself. - NPV depends on the prevalence of disease in the population, whereas specificity is an intrinsic property of the test.

Question 1677: A new screening test for breast cancer has a sensitivity of 90% and a specificity of 95%. In a population with moderate breast cancer prevalence, what does a positive test result most likely indicate?

A. Indicates a low likelihood of breast cancer
B. Cannot be determined without knowing disease prevalence (Correct Answer)
C. Indicates a false negative result
D. Indicates a high likelihood of breast cancer

Explanation: ***Cannot be determined without knowing disease prevalence*** - While the test has **high sensitivity (90%)** and **high specificity (95%)**, the interpretation of a positive test result depends critically on the **Positive Predictive Value (PPV)**. - **PPV formula:** PPV = (Sensitivity × Prevalence) / [(Sensitivity × Prevalence) + (1 - Specificity) × (1 - Prevalence)] - The term **"moderate prevalence"** is too vague to calculate PPV accurately: - At **1% prevalence**: PPV ≈ 15% (positive test = low likelihood of disease) - At **10% prevalence**: PPV ≈ 67% (positive test = moderate likelihood) - At **50% prevalence**: PPV ≈ 95% (positive test = high likelihood) - Without knowing the **exact prevalence**, we cannot definitively interpret what a positive result means. *Indicates a high likelihood of breast cancer* - This would only be true if the prevalence were **high (>30-40%)**, but "moderate" is undefined. - **High specificity helps** reduce false positives, but PPV still varies significantly with prevalence in the moderate range. *Indicates a low likelihood of breast cancer* - This would be true at **very low prevalence (<5%)**, but we cannot assume this from "moderate." - The **high specificity (95%)** does reduce false positives compared to a less specific test. *Indicates a false negative result* - A **false negative** occurs when the test is **negative** in a person who actually has the disease. - The question asks about a **positive test result**, not a negative one.

Question 1678: In a population with a disease prevalence of 5%, a new screening test for a disease has a sensitivity of 92% and a specificity of 85%. What can be inferred about the positive predictive value (PPV) and negative predictive value (NPV) of this test?

A. Both PPV and NPV are low.
B. PPV is high, NPV is low.
C. Both PPV and NPV are high.
D. PPV is low, NPV is high. (Correct Answer)

Explanation: ***PPV is low, NPV is high.*** - In populations with **low disease prevalence** (5% in this case), the **positive predictive value (PPV)** tends to be low, even with good test sensitivity and specificity, because many positive test results will be false positives. - Conversely, with a low prevalence and good specificity (85%), the **negative predictive value (NPV)** tends to be high, meaning a negative test result is very likely to be truly negative. *Both PPV and NPV are low.* - The **NPV** is expected to be high, not low, given the good specificity and low disease prevalence. - A low NPV would indicate that negative test results are often incorrect, which is unlikely under these conditions. *PPV is high, NPV is low.* - The **PPV** is expected to be low, not high, due to the low disease prevalence leading to a higher proportion of false positives among positive tests. - The **NPV** is expected to be high, not low, due to the low prevalence and good specificity, making negative results reliable. *Both PPV and NPV are high.* - The **PPV** is unlikely to be high given the very low disease prevalence, as a significant number of positive tests would be false positives. - While NPV is high, the low PPV prevents both from being high simultaneously in this scenario.

Question 1679: In a case-control study on hypertension and stroke, which measure is used to compare the risk between cases and controls?

A. Odds ratio (Correct Answer)
B. Relative risk
C. Absolute risk reduction
D. Incidence rate

Explanation: ***Odds ratio*** - In a **case-control study**, we start by identifying individuals with the outcome (cases) and those without (controls) and then look back to assess their exposure status. - The **odds ratio (OR)** quantifies the odds of exposure among cases relative to the odds of exposure among controls, thereby estimating the strength of association between exposure and outcome. *Relative risk* - **Relative risk (RR)** is used in **cohort studies** and **randomized controlled trials** to compare the risk of an outcome in an exposed group versus an unexposed group. - It directly measures how many times more likely an exposed group is to develop an outcome compared to an unexposed group. *Absolute risk reduction* - **Absolute risk reduction (ARR)** is a measure of the difference in risk between an intervention group and a control group, typically used in **clinical trials**. - It indicates the **absolute difference in the proportion of people** who experience an event across two groups, reflecting the direct benefit of an intervention. *Incidence rate* - The **incidence rate** measures the number of new cases of a disease or outcome that develop in a population at risk over a specified period. - It is used to describe the **frequency of disease occurrence** in a population, not to compare risk between cases and controls.

Question 1680: Which of the following is a measure of incidence in epidemiology?

A. Prevalence
B. Incidence Rate (Correct Answer)
C. Case Fatality Rate
D. Mortality Rate

Explanation: ***Incidence Rate*** - **Incidence rate** measures the speed at which new cases of a disease occur in a population at risk over a specified period. - It is calculated as the **number of new cases** divided by the total person-time at risk. *Prevalence* - **Prevalence** measures the proportion of individuals in a population who have a disease at a specific point in time or over a period. - It includes **both new and existing cases** and does not directly measure the rate of new disease occurrence. *Case Fatality Rate* - The **case fatality rate** (CFR) is the proportion of individuals diagnosed with a disease who die from that disease. - It is a measure of the **severity of a disease** and not a measure of how frequently new cases arise. *Mortality Rate* - The **mortality rate** measures the frequency of death in a defined population during a specified period. - While related to disease, it focuses on **deaths** rather than the occurrence of new disease cases.

Practice by Chapter

Principles of Epidemiology

Practice Questions

Measures of Disease Frequency

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Epidemiological Study Designs

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Descriptive Epidemiology

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Analytical Epidemiology

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Experimental Epidemiology

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Screening for Disease

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Surveillance Systems

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Investigation of an Epidemic

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Association and Causation

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Modern Epidemiological Methods

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Critical Appraisal of Epidemiological Studies

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